Overview

Dataset statistics

Number of variables37
Number of observations102
Missing cells1311
Missing cells (%)34.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.8 KiB
Average record size in memory560.4 B

Variable types

NUM21
CAT8
UNSUPPORTED5
BOOL2
URL1

Warnings

datetime_submitted has 100 (98.0%) missing values Missing
comments has 101 (99.0%) missing values Missing
pdfs has 100 (98.0%) missing values Missing
total_amount has 100 (98.0%) missing values Missing
customer_id has 100 (98.0%) missing values Missing
ip_address has 100 (98.0%) missing values Missing
id has 100 (98.0%) missing values Missing
utm_source has 102 (100.0%) missing values Missing
utm_medium has 102 (100.0%) missing values Missing
utm_campaign has 102 (100.0%) missing values Missing
utm_term has 102 (100.0%) missing values Missing
utm_content has 102 (100.0%) missing values Missing
device_type has 100 (98.0%) missing values Missing
datetime_submitted is uniformly distributed Uniform
ip_address is uniformly distributed Uniform
id is uniformly distributed Uniform
utm_source is an unsupported type, check if it needs cleaning or further analysis Unsupported
utm_medium is an unsupported type, check if it needs cleaning or further analysis Unsupported
utm_campaign is an unsupported type, check if it needs cleaning or further analysis Unsupported
utm_term is an unsupported type, check if it needs cleaning or further analysis Unsupported
utm_content is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2020-09-12 03:40:35.186644
Analysis finished2020-09-12 03:41:52.480774
Duration1 minute and 17.29 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Categorical

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2
100 
1
 
1
0
 
1
ValueCountFrequency (%) 
210098.0%
 
111.0%
 
011.0%
 
2020-09-11T23:41:52.613044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)2.0%
2020-09-11T23:41:52.747524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:52.893553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
210098.0%
 
011.0%
 
111.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number102100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
210098.0%
 
011.0%
 
111.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common102100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
210098.0%
 
011.0%
 
111.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
210098.0%
 
011.0%
 
111.0%
 

datetime_submitted
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
2020-09-09 18:06:51
2020-09-11 21:24:06
ValueCountFrequency (%) 
2020-09-09 18:06:5111.0%
 
2020-09-11 21:24:0611.0%
 
(Missing)10098.0%
 
2020-09-11T23:41:53.068596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)100.0%
2020-09-11T23:41:53.197293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:53.342515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length3
Mean length3.31372549
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20059.2%
 
a10029.6%
 
092.7%
 
261.8%
 
151.5%
 
-41.2%
 
:41.2%
 
930.9%
 
20.6%
 
620.6%
 
410.3%
 
810.3%
 
510.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter30088.8%
 
Decimal Number288.3%
 
Dash Punctuation41.2%
 
Other Punctuation41.2%
 
Space Separator20.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0932.1%
 
2621.4%
 
1517.9%
 
9310.7%
 
627.1%
 
413.6%
 
813.6%
 
513.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:4100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin30088.8%
 
Common3811.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
0923.7%
 
2615.8%
 
1513.2%
 
-410.5%
 
:410.5%
 
937.9%
 
25.3%
 
625.3%
 
412.6%
 
812.6%
 
512.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII338100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20059.2%
 
a10029.6%
 
092.7%
 
261.8%
 
151.5%
 
-41.2%
 
:41.2%
 
930.9%
 
20.6%
 
620.6%
 
410.3%
 
810.3%
 
510.3%
 

grade
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.931372549
Minimum8
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:53.511830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q19
median10
Q311
95-th percentile12
Maximum12
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.314502533
Coefficient of variation (CV)0.1323585966
Kurtosis-1.076969281
Mean9.931372549
Median Absolute Deviation (MAD)1
Skewness0.1021015033
Sum1013
Variance1.727916909
MonotocityNot monotonic
2020-09-11T23:41:53.662932image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
102625.5%
 
92423.5%
 
111918.6%
 
81716.7%
 
121615.7%
 
ValueCountFrequency (%) 
81716.7%
 
92423.5%
 
102625.5%
 
111918.6%
 
121615.7%
 
ValueCountFrequency (%) 
121615.7%
 
111918.6%
 
102625.5%
 
92423.5%
 
81716.7%
 

gender
Boolean

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
59 
1
43 
ValueCountFrequency (%) 
05957.8%
 
14342.2%
 
2020-09-11T23:41:53.809467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

ethnicity
Categorical

Distinct4
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
w
28 
a
28 
l
23 
b
23 
ValueCountFrequency (%) 
w2827.5%
 
a2827.5%
 
l2322.5%
 
b2322.5%
 
2020-09-11T23:41:53.964112image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-11T23:41:54.105249image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:54.255283image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
w2827.5%
 
a2827.5%
 
b2322.5%
 
l2322.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter102100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
w2827.5%
 
a2827.5%
 
b2322.5%
 
l2322.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin102100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
w2827.5%
 
a2827.5%
 
b2322.5%
 
l2322.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII102100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
w2827.5%
 
a2827.5%
 
b2322.5%
 
l2322.5%
 

tenure
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.009803922
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:54.401686image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.389458888
Coefficient of variation (CV)0.461644321
Kurtosis-1.255824205
Mean3.009803922
Median Absolute Deviation (MAD)1
Skewness-0.01782670274
Sum307
Variance1.930596001
MonotocityNot monotonic
2020-09-11T23:41:54.553934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
42221.6%
 
32120.6%
 
22120.6%
 
51918.6%
 
11918.6%
 
ValueCountFrequency (%) 
11918.6%
 
22120.6%
 
32120.6%
 
42221.6%
 
51918.6%
 
ValueCountFrequency (%) 
51918.6%
 
42221.6%
 
32120.6%
 
22120.6%
 
11918.6%
 

scl_mission_cnct
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.862745098
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:54.705915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.414488087
Coefficient of variation (CV)0.494102003
Kurtosis-1.295980053
Mean2.862745098
Median Absolute Deviation (MAD)1
Skewness0.2264991582
Sum292
Variance2.000776548
MonotocityNot monotonic
2020-09-11T23:41:54.866254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
23029.4%
 
12019.6%
 
51918.6%
 
41817.6%
 
31514.7%
 
ValueCountFrequency (%) 
12019.6%
 
23029.4%
 
31514.7%
 
41817.6%
 
51918.6%
 
ValueCountFrequency (%) 
51918.6%
 
41817.6%
 
31514.7%
 
23029.4%
 
12019.6%
 

scl_christ_centered
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.892156863
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:55.022061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.392389944
Coefficient of variation (CV)0.4814365232
Kurtosis-1.202648097
Mean2.892156863
Median Absolute Deviation (MAD)1
Skewness0.1065436256
Sum295
Variance1.938749757
MonotocityNot monotonic
2020-09-11T23:41:55.178682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32524.5%
 
12221.6%
 
22019.6%
 
51817.6%
 
41716.7%
 
ValueCountFrequency (%) 
12221.6%
 
22019.6%
 
32524.5%
 
41716.7%
 
51817.6%
 
ValueCountFrequency (%) 
51817.6%
 
41716.7%
 
32524.5%
 
22019.6%
 
12221.6%
 

scl_happy
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.019607843
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:55.340065image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.41407628
Coefficient of variation (CV)0.4682979889
Kurtosis-1.267955248
Mean3.019607843
Median Absolute Deviation (MAD)1
Skewness-0.01386063841
Sum308
Variance1.999611726
MonotocityNot monotonic
2020-09-11T23:41:55.491060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32322.5%
 
52120.6%
 
12019.6%
 
41918.6%
 
21918.6%
 
ValueCountFrequency (%) 
12019.6%
 
21918.6%
 
32322.5%
 
41918.6%
 
52120.6%
 
ValueCountFrequency (%) 
52120.6%
 
41918.6%
 
32322.5%
 
21918.6%
 
12019.6%
 

scl_speakup
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.039215686
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:55.651760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.37099782
Coefficient of variation (CV)0.4511025085
Kurtosis-1.243667097
Mean3.039215686
Median Absolute Deviation (MAD)1
Skewness-0.02484857449
Sum310
Variance1.879635022
MonotocityNot monotonic
2020-09-11T23:41:55.808816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
42322.5%
 
22322.5%
 
32019.6%
 
51918.6%
 
11716.7%
 
ValueCountFrequency (%) 
11716.7%
 
22322.5%
 
32019.6%
 
42322.5%
 
51918.6%
 
ValueCountFrequency (%) 
51918.6%
 
42322.5%
 
32019.6%
 
22322.5%
 
11716.7%
 

scl_mission_diverse
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.176470588
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:55.964861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34.75
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.458412026
Coefficient of variation (CV)0.4591297119
Kurtosis-1.372897378
Mean3.176470588
Median Absolute Deviation (MAD)1
Skewness-0.1572176156
Sum324
Variance2.126965638
MonotocityNot monotonic
2020-09-11T23:41:56.125602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
52625.5%
 
42221.6%
 
22019.6%
 
11817.6%
 
31615.7%
 
ValueCountFrequency (%) 
11817.6%
 
22019.6%
 
31615.7%
 
42221.6%
 
52625.5%
 
ValueCountFrequency (%) 
52625.5%
 
42221.6%
 
31615.7%
 
22019.6%
 
11817.6%
 

scl_ldrshp
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.049019608
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:56.284782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.485090596
Coefficient of variation (CV)0.4870715139
Kurtosis-1.445874572
Mean3.049019608
Median Absolute Deviation (MAD)1
Skewness-0.03012440295
Sum311
Variance2.205494079
MonotocityNot monotonic
2020-09-11T23:41:56.446311image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
52423.5%
 
22221.6%
 
42120.6%
 
12120.6%
 
31413.7%
 
ValueCountFrequency (%) 
12120.6%
 
22221.6%
 
31413.7%
 
42120.6%
 
52423.5%
 
ValueCountFrequency (%) 
52423.5%
 
42120.6%
 
31413.7%
 
22221.6%
 
12120.6%
 

scl_celebr
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.058823529
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:56.783536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.384665162
Coefficient of variation (CV)0.4526789954
Kurtosis-1.217806096
Mean3.058823529
Median Absolute Deviation (MAD)1
Skewness-0.1528985129
Sum312
Variance1.917297612
MonotocityNot monotonic
2020-09-11T23:41:56.935233image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
42625.5%
 
32221.6%
 
12019.6%
 
51817.6%
 
21615.7%
 
ValueCountFrequency (%) 
12019.6%
 
21615.7%
 
32221.6%
 
42625.5%
 
51817.6%
 
ValueCountFrequency (%) 
51817.6%
 
42625.5%
 
32221.6%
 
21615.7%
 
12019.6%
 

scl_belong
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.745098039
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:57.088604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.39792119
Coefficient of variation (CV)0.5092427192
Kurtosis-1.235267355
Mean2.745098039
Median Absolute Deviation (MAD)1
Skewness0.2462883297
Sum280
Variance1.954183654
MonotocityNot monotonic
2020-09-11T23:41:57.242897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
22524.5%
 
12524.5%
 
41918.6%
 
31817.6%
 
51514.7%
 
ValueCountFrequency (%) 
12524.5%
 
22524.5%
 
31817.6%
 
41918.6%
 
51514.7%
 
ValueCountFrequency (%) 
51514.7%
 
41918.6%
 
31817.6%
 
22524.5%
 
12524.5%
 

scl_neg_bully
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.87254902
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:57.395111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.376403468
Coefficient of variation (CV)0.4791575214
Kurtosis-1.289463869
Mean2.87254902
Median Absolute Deviation (MAD)1
Skewness-0.02179779589
Sum293
Variance1.894486507
MonotocityNot monotonic
2020-09-11T23:41:57.548468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
42726.5%
 
12423.5%
 
32019.6%
 
21817.6%
 
51312.7%
 
ValueCountFrequency (%) 
12423.5%
 
21817.6%
 
32019.6%
 
42726.5%
 
51312.7%
 
ValueCountFrequency (%) 
51312.7%
 
42726.5%
 
32019.6%
 
21817.6%
 
12423.5%
 

scl_getalong
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.156862745
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:57.705288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.539866897
Coefficient of variation (CV)0.4877839239
Kurtosis-1.496898777
Mean3.156862745
Median Absolute Deviation (MAD)1.5
Skewness-0.1523846244
Sum322
Variance2.37119006
MonotocityNot monotonic
2020-09-11T23:41:57.864677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
52928.4%
 
12221.6%
 
42019.6%
 
21817.6%
 
31312.7%
 
ValueCountFrequency (%) 
12221.6%
 
21817.6%
 
31312.7%
 
42019.6%
 
52928.4%
 
ValueCountFrequency (%) 
52928.4%
 
42019.6%
 
31312.7%
 
21817.6%
 
12221.6%
 

scl_rules
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.921568627
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:58.020334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.376509249
Coefficient of variation (CV)0.4711541725
Kurtosis-1.262667332
Mean2.921568627
Median Absolute Deviation (MAD)1
Skewness0.0971035498
Sum298
Variance1.894777713
MonotocityNot monotonic
2020-09-11T23:41:58.175320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
22625.5%
 
42221.6%
 
11918.6%
 
31817.6%
 
51716.7%
 
ValueCountFrequency (%) 
11918.6%
 
22625.5%
 
31817.6%
 
42221.6%
 
51716.7%
 
ValueCountFrequency (%) 
51716.7%
 
42221.6%
 
31817.6%
 
22625.5%
 
11918.6%
 

scl_events
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.029411765
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:58.326632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.338359291
Coefficient of variation (CV)0.4417885037
Kurtosis-1.155788679
Mean3.029411765
Median Absolute Deviation (MAD)1
Skewness-0.1051717587
Sum309
Variance1.791205591
MonotocityNot monotonic
2020-09-11T23:41:58.496204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
42625.5%
 
32322.5%
 
21918.6%
 
11817.6%
 
51615.7%
 
ValueCountFrequency (%) 
11817.6%
 
21918.6%
 
32322.5%
 
42625.5%
 
51615.7%
 
ValueCountFrequency (%) 
51615.7%
 
42625.5%
 
32322.5%
 
21918.6%
 
11817.6%
 

scl_discipline
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.882352941
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:58.660681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.366459019
Coefficient of variation (CV)0.4740776188
Kurtosis-1.120412847
Mean2.882352941
Median Absolute Deviation (MAD)1
Skewness0.09787102412
Sum294
Variance1.86721025
MonotocityNot monotonic
2020-09-11T23:41:58.818563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32928.4%
 
12221.6%
 
21817.6%
 
51716.7%
 
41615.7%
 
ValueCountFrequency (%) 
12221.6%
 
21817.6%
 
32928.4%
 
41615.7%
 
51716.7%
 
ValueCountFrequency (%) 
51716.7%
 
41615.7%
 
32928.4%
 
21817.6%
 
12221.6%
 

scl_equipped
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.176470588
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:58.974288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.471927171
Coefficient of variation (CV)0.4633844799
Kurtosis-1.389755923
Mean3.176470588
Median Absolute Deviation (MAD)1
Skewness-0.1216311931
Sum324
Variance2.166569598
MonotocityNot monotonic
2020-09-11T23:41:59.127744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
52827.5%
 
22019.6%
 
41817.6%
 
31817.6%
 
11817.6%
 
ValueCountFrequency (%) 
11817.6%
 
22019.6%
 
31817.6%
 
41817.6%
 
52827.5%
 
ValueCountFrequency (%) 
52827.5%
 
41817.6%
 
31817.6%
 
22019.6%
 
11817.6%
 

scl_learn
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.931372549
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:59.283677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.457380017
Coefficient of variation (CV)0.4971664271
Kurtosis-1.34992973
Mean2.931372549
Median Absolute Deviation (MAD)1
Skewness-0.01566436899
Sum299
Variance2.123956513
MonotocityNot monotonic
2020-09-11T23:41:59.433786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
12625.5%
 
32221.6%
 
42120.6%
 
51918.6%
 
21413.7%
 
ValueCountFrequency (%) 
12625.5%
 
21413.7%
 
32221.6%
 
42120.6%
 
51918.6%
 
ValueCountFrequency (%) 
51918.6%
 
42120.6%
 
32221.6%
 
21413.7%
 
12625.5%
 

scl_cared
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.921568627
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:59.589798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.51354462
Coefficient of variation (CV)0.5180588968
Kurtosis-1.46890415
Mean2.921568627
Median Absolute Deviation (MAD)1
Skewness0.1004578627
Sum298
Variance2.290817317
MonotocityNot monotonic
2020-09-11T23:41:59.744833image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
12524.5%
 
52322.5%
 
22221.6%
 
41817.6%
 
31413.7%
 
ValueCountFrequency (%) 
12524.5%
 
22221.6%
 
31413.7%
 
41817.6%
 
52322.5%
 
ValueCountFrequency (%) 
52322.5%
 
41817.6%
 
31413.7%
 
22221.6%
 
12524.5%
 

scl_chapel
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.009803922
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:41:59.898071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.431575315
Coefficient of variation (CV)0.475637401
Kurtosis-1.279386058
Mean3.009803922
Median Absolute Deviation (MAD)1
Skewness-0.1207800881
Sum307
Variance2.049407882
MonotocityNot monotonic
2020-09-11T23:42:00.062207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32423.5%
 
12423.5%
 
42322.5%
 
51918.6%
 
21211.8%
 
ValueCountFrequency (%) 
12423.5%
 
21211.8%
 
32423.5%
 
42322.5%
 
51918.6%
 
ValueCountFrequency (%) 
51918.6%
 
42322.5%
 
32423.5%
 
21211.8%
 
12423.5%
 

scl_cultures
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.794117647
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:42:00.215584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.437394751
Coefficient of variation (CV)0.514436016
Kurtosis-1.23699598
Mean2.794117647
Median Absolute Deviation (MAD)1
Skewness0.3699081388
Sum285
Variance2.066103669
MonotocityNot monotonic
2020-09-11T23:42:00.373724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
23332.4%
 
52120.6%
 
12120.6%
 
31514.7%
 
41211.8%
 
ValueCountFrequency (%) 
12120.6%
 
23332.4%
 
31514.7%
 
41211.8%
 
52120.6%
 
ValueCountFrequency (%) 
52120.6%
 
41211.8%
 
31514.7%
 
23332.4%
 
12120.6%
 

scl_rlmdls
Real number (ℝ≥0)

Distinct5
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.960784314
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-09-11T23:42:00.531161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.341799948
Coefficient of variation (CV)0.453190711
Kurtosis-1.169290128
Mean2.960784314
Median Absolute Deviation (MAD)1
Skewness0.1229008438
Sum302
Variance1.800427102
MonotocityNot monotonic
2020-09-11T23:42:00.689327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
22726.5%
 
32221.6%
 
41918.6%
 
51817.6%
 
11615.7%
 
ValueCountFrequency (%) 
11615.7%
 
22726.5%
 
32221.6%
 
41918.6%
 
51817.6%
 
ValueCountFrequency (%) 
51817.6%
 
41918.6%
 
32221.6%
 
22726.5%
 
11615.7%
 

comments
Categorical

MISSING

Distinct1
Distinct (%)100.0%
Missing101
Missing (%)99.0%
Memory size1.6 KiB
I am a very unhappy person. Everyone at this school is dumb.
ValueCountFrequency (%) 
I am a very unhappy person. Everyone at this school is dumb.11.0%
 
(Missing)10199.0%
 
2020-09-11T23:42:00.854813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)100.0%
2020-09-11T23:42:01.153985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:42:01.293649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length3
Mean length3.558823529
Min length3

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20556.5%
 
a10528.9%
 
113.0%
 
e41.1%
 
s41.1%
 
o41.1%
 
r30.8%
 
y30.8%
 
h30.8%
 
p30.8%
 
m20.6%
 
v20.6%
 
u20.6%
 
.20.6%
 
t20.6%
 
i20.6%
 
I10.3%
 
E10.3%
 
c10.3%
 
l10.3%
 
d10.3%
 
b10.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter34895.9%
 
Space Separator113.0%
 
Uppercase Letter20.6%
 
Other Punctuation20.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I150.0%
 
E150.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20558.9%
 
a10530.2%
 
e41.1%
 
s41.1%
 
o41.1%
 
r30.9%
 
y30.9%
 
h30.9%
 
p30.9%
 
m20.6%
 
v20.6%
 
u20.6%
 
t20.6%
 
i20.6%
 
c10.3%
 
l10.3%
 
d10.3%
 
b10.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35096.4%
 
Common133.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20558.6%
 
a10530.0%
 
e41.1%
 
s41.1%
 
o41.1%
 
r30.9%
 
y30.9%
 
h30.9%
 
p30.9%
 
m20.6%
 
v20.6%
 
u20.6%
 
t20.6%
 
i20.6%
 
I10.3%
 
E10.3%
 
c10.3%
 
l10.3%
 
d10.3%
 
b10.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
1184.6%
 
.215.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII363100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20556.5%
 
a10528.9%
 
113.0%
 
e41.1%
 
s41.1%
 
o41.1%
 
r30.8%
 
y30.8%
 
h30.8%
 
p30.8%
 
m20.6%
 
v20.6%
 
u20.6%
 
.20.6%
 
t20.6%
 
i20.6%
 
I10.3%
 
E10.3%
 
c10.3%
 
l10.3%
 
d10.3%
 
b10.3%
 

pdfs
URL

MISSING

Distinct2
Distinct (%)100.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
https://s3.amazonaws.com/pf-pdf-01/79525/2020-09-11/jb13ygb/CP7g1yPOBEM9hr8oZMMI/submission-results.pdf
 
1
https://s3.amazonaws.com/pf-pdf-01/79525/2020-09-11/jb13ygb/DSa4qfxbSc7q3ozlSGN6/submission-results.pdf
 
1
(Missing)
100 
ValueCountFrequency (%) 
https://s3.amazonaws.com/pf-pdf-01/79525/2020-09-11/jb13ygb/CP7g1yPOBEM9hr8oZMMI/submission-results.pdf11.0%
 
https://s3.amazonaws.com/pf-pdf-01/79525/2020-09-11/jb13ygb/DSa4qfxbSc7q3ozlSGN6/submission-results.pdf11.0%
 
(Missing)10098.0%
 
ValueCountFrequency (%) 
https22.0%
 
(Missing)10098.0%
 
ValueCountFrequency (%) 
s3.amazonaws.com22.0%
 
(Missing)10098.0%
 
ValueCountFrequency (%) 
/pf-pdf-01/79525/2020-09-11/jb13ygb/DSa4qfxbSc7q3ozlSGN6/submission-results.pdf11.0%
 
/pf-pdf-01/79525/2020-09-11/jb13ygb/CP7g1yPOBEM9hr8oZMMI/submission-results.pdf11.0%
 
(Missing)10098.0%
 
ValueCountFrequency (%) 
22.0%
 
(Missing)10098.0%
 
ValueCountFrequency (%) 
22.0%
 
(Missing)10098.0%
 

total_amount
Boolean

MISSING

Distinct1
Distinct (%)50.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
0
 
2
(Missing)
100 
ValueCountFrequency (%) 
022.0%
 
(Missing)10098.0%
 
2020-09-11T23:42:01.421536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

customer_id
Categorical

MISSING

Distinct1
Distinct (%)50.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
-
ValueCountFrequency (%) 
-22.0%
 
(Missing)10098.0%
 
2020-09-11T23:42:01.557243image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-11T23:42:01.690021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:42:01.825015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.980392157
Min length2

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20065.8%
 
a10032.9%
 
20.7%
 
-20.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter30098.7%
 
Space Separator20.7%
 
Dash Punctuation20.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin30098.7%
 
Common41.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
250.0%
 
-250.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII304100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20065.8%
 
a10032.9%
 
20.7%
 
-20.7%
 

ip_address
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
72.214.79.148
98.169.34.10
ValueCountFrequency (%) 
72.214.79.14811.0%
 
98.169.34.1011.0%
 
(Missing)10098.0%
 
2020-09-11T23:42:01.990616image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)100.0%
2020-09-11T23:42:02.118288image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:42:02.262783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length3
Mean length3.18627451
Min length3

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20061.5%
 
a10030.8%
 
.61.8%
 
141.2%
 
930.9%
 
430.9%
 
820.6%
 
720.6%
 
220.6%
 
610.3%
 
310.3%
 
010.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter30092.3%
 
Decimal Number195.8%
 
Other Punctuation61.8%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1421.1%
 
9315.8%
 
4315.8%
 
8210.5%
 
7210.5%
 
2210.5%
 
615.3%
 
315.3%
 
015.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.6100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin30092.3%
 
Common257.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
.624.0%
 
1416.0%
 
9312.0%
 
4312.0%
 
828.0%
 
728.0%
 
228.0%
 
614.0%
 
314.0%
 
014.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20066.7%
 
a10033.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII325100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20061.5%
 
a10030.8%
 
.61.8%
 
141.2%
 
930.9%
 
430.9%
 
820.6%
 
720.6%
 
220.6%
 
610.3%
 
310.3%
 
010.3%
 

id
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
5f5919bb9592df2e570dae7b
5f5beaf6d3598014ac21893e
ValueCountFrequency (%) 
5f5919bb9592df2e570dae7b11.0%
 
5f5beaf6d3598014ac21893e11.0%
 
(Missing)10098.0%
 
2020-09-11T23:42:02.429131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)100.0%
2020-09-11T23:42:02.561713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:42:02.706120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length3
Mean length3.411764706
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20057.5%
 
a10329.6%
 
572.0%
 
961.7%
 
f41.1%
 
b41.1%
 
e41.1%
 
d30.9%
 
130.9%
 
230.9%
 
320.6%
 
820.6%
 
020.6%
 
720.6%
 
610.3%
 
410.3%
 
c10.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter31991.7%
 
Decimal Number298.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
5724.1%
 
9620.7%
 
1310.3%
 
2310.3%
 
326.9%
 
826.9%
 
026.9%
 
726.9%
 
613.4%
 
413.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20062.7%
 
a10332.3%
 
f41.3%
 
b41.3%
 
e41.3%
 
d30.9%
 
c10.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin31991.7%
 
Common298.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
5724.1%
 
9620.7%
 
1310.3%
 
2310.3%
 
326.9%
 
826.9%
 
026.9%
 
726.9%
 
613.4%
 
413.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20062.7%
 
a10332.3%
 
f41.3%
 
b41.3%
 
e41.3%
 
d30.9%
 
c10.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII348100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20057.5%
 
a10329.6%
 
572.0%
 
961.7%
 
f41.1%
 
b41.1%
 
e41.1%
 
d30.9%
 
130.9%
 
230.9%
 
320.6%
 
820.6%
 
020.6%
 
720.6%
 
610.3%
 
410.3%
 
c10.3%
 

utm_source
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing102
Missing (%)100.0%
Memory size1.6 KiB

utm_medium
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing102
Missing (%)100.0%
Memory size1.6 KiB

utm_campaign
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing102
Missing (%)100.0%
Memory size1.6 KiB

utm_term
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing102
Missing (%)100.0%
Memory size1.6 KiB

utm_content
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing102
Missing (%)100.0%
Memory size1.6 KiB

device_type
Categorical

MISSING

Distinct1
Distinct (%)50.0%
Missing100
Missing (%)98.0%
Memory size1.6 KiB
desktop
ValueCountFrequency (%) 
desktop22.0%
 
(Missing)10098.0%
 
2020-09-11T23:42:02.860539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-11T23:42:02.989581image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:42:03.133958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.078431373
Min length3

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n20063.7%
 
a10031.8%
 
d20.6%
 
e20.6%
 
s20.6%
 
k20.6%
 
t20.6%
 
o20.6%
 
p20.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter314100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n20063.7%
 
a10031.8%
 
d20.6%
 
e20.6%
 
s20.6%
 
k20.6%
 
t20.6%
 
o20.6%
 
p20.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin314100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n20063.7%
 
a10031.8%
 
d20.6%
 
e20.6%
 
s20.6%
 
k20.6%
 
t20.6%
 
o20.6%
 
p20.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII314100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n20063.7%
 
a10031.8%
 
d20.6%
 
e20.6%
 
s20.6%
 
k20.6%
 
t20.6%
 
o20.6%
 
p20.6%
 

Interactions

2020-09-11T23:40:38.637779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:38.795817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:38.953356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.110391image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.266387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.422456image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.579520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.739484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:39.894579image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.055422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.215396image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.373253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.529068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.686972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:40.843190image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:41.008373image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:41.161908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:41.315858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:43.684109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:53.378326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:53.845538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:54.157201image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:40:54.315004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:54.788877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:55.097075image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:55.886088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:40:56.196725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-09-11T23:41:37.439751image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:37.594416image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:37.750615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:37.906713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:38.064018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:38.225170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:38.377851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:38.537211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:38.696976image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.018626image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.175999image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.331669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.487161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.641541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.801488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:39.961111image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.113901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.280823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.452561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.617127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.774257image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:40.930239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.081590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.238651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.396056image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.551385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.711910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:41.865281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.020030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.175765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.335001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.489535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.645659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.802694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:42.956851image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:43.117173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:43.437271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:43.592140image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:43.745350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:43.898533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.060669image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.216827image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.371018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.530158image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.691218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:44.849544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.007239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.161342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.315794image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.476666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.630796image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.792533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:45.952329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.106732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.265569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.417608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.594281image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.750047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:46.904810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:47.058130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:47.214346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:47.369462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:47.529959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:47.856218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.015382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.177143image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.335856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.495869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.653437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.809478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:48.968847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.123096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.277652image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.434026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.594072image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.752366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:49.916775image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:50.072991image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:50.243638image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:50.403031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:50.561527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-11T23:42:03.326095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-11T23:42:03.652744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-11T23:42:03.955223image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-11T23:42:04.269078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-11T23:42:04.528925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-11T23:41:50.883707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:51.666357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:51.947812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T23:41:52.347363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

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Last rows

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